Overview

Dataset statistics

Number of variables16
Number of observations63
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)1.6%
Total size in memory8.4 KiB
Average record size in memory136.0 B

Variable types

Numeric15
Categorical1

Alerts

Dataset has 1 (1.6%) duplicate rowsDuplicates
METABOLITE 0 is highly overall correlated with METABOLITE 1 and 11 other fieldsHigh correlation
METABOLITE 1 is highly overall correlated with METABOLITE 0 and 12 other fieldsHigh correlation
METABOLITE 2 is highly overall correlated with METABOLITE 0 and 13 other fieldsHigh correlation
METABOLITE 3 is highly overall correlated with METABOLITE 0 and 13 other fieldsHigh correlation
METABOLITE 4 is highly overall correlated with METABOLITE 0 and 11 other fieldsHigh correlation
METABOLITE 5 is highly overall correlated with METABOLITE 0 and 13 other fieldsHigh correlation
METABOLITE 6 is highly overall correlated with METABOLITE 0 and 13 other fieldsHigh correlation
METABOLITE 7 is highly overall correlated with METABOLITE 0 and 12 other fieldsHigh correlation
METABOLITE 8 is highly overall correlated with METABOLITE 0 and 12 other fieldsHigh correlation
METABOLITE 9 is highly overall correlated with METABOLITE 1 and 9 other fieldsHigh correlation
METABOLITE 10 is highly overall correlated with METABOLITE 0 and 11 other fieldsHigh correlation
METABOLITE 11 is highly overall correlated with METABOLITE 2 and 5 other fieldsHigh correlation
METABOLITE 12 is highly overall correlated with METABOLITE 0 and 13 other fieldsHigh correlation
METABOLITE 13 is highly overall correlated with METABOLITE 0 and 13 other fieldsHigh correlation
METABOLITE 14 is highly overall correlated with METABOLITE 0 and 10 other fieldsHigh correlation

Reproduction

Analysis started2023-10-16 17:59:22.800883
Analysis finished2023-10-16 18:00:27.637502
Duration1 minute and 4.84 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

METABOLITE 0
Real number (ℝ)

HIGH CORRELATION 

Distinct62
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.89757614
Minimum-0.25570496
Maximum1.9074532
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)4.8%
Memory size1008.0 B
2023-10-16T20:00:27.826192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.25570496
5-th percentile0.059008629
Q10.49368502
median0.89515216
Q31.293577
95-th percentile1.6863258
Maximum1.9074532
Range2.1631582
Interquartile range (IQR)0.799892

Descriptive statistics

Standard deviation0.54145659
Coefficient of variation (CV)0.60324307
Kurtosis-0.84395646
Mean0.89757614
Median Absolute Deviation (MAD)0.41056964
Skewness-0.11353469
Sum56.547297
Variance0.29317524
MonotonicityNot monotonic
2023-10-16T20:00:29.011626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.452869757 2
 
3.2%
1.507485706 1
 
1.6%
1.541666923 1
 
1.6%
1.671493954 1
 
1.6%
0.3226945007 1
 
1.6%
1.56711569 1
 
1.6%
0.08698385202 1
 
1.6%
0.5797557018 1
 
1.6%
0.5349057935 1
 
1.6%
1.310113883 1
 
1.6%
Other values (52) 52
82.5%
ValueCountFrequency (%)
-0.2557049628 1
1.6%
-0.190198383 1
1.6%
-0.02431565517 1
1.6%
0.05590027107 1
1.6%
0.08698385202 1
1.6%
0.1510846284 1
1.6%
0.1579401511 1
1.6%
0.2271325444 1
1.6%
0.2336812515 1
1.6%
0.3226945007 1
1.6%
ValueCountFrequency (%)
1.90745319 1
1.6%
1.893777335 1
1.6%
1.756200398 1
1.6%
1.687973836 1
1.6%
1.671493954 1
1.6%
1.648198178 1
1.6%
1.56711569 1
1.6%
1.55679231 1
1.6%
1.541666923 1
1.6%
1.507485706 1
1.6%

METABOLITE 1
Real number (ℝ)

HIGH CORRELATION 

Distinct62
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0489948
Minimum-0.3417621
Maximum2.1177288
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)1.6%
Memory size1008.0 B
2023-10-16T20:00:31.167518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.3417621
5-th percentile0.17100518
Q10.59309095
median1.0972481
Q31.463798
95-th percentile1.9368497
Maximum2.1177288
Range2.4594909
Interquartile range (IQR)0.87070703

Descriptive statistics

Standard deviation0.57498131
Coefficient of variation (CV)0.54812597
Kurtosis-0.74595068
Mean1.0489948
Median Absolute Deviation (MAD)0.43085351
Skewness-0.16235178
Sum66.086674
Variance0.33060351
MonotonicityNot monotonic
2023-10-16T20:00:33.231348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.152910444 2
 
3.2%
1.117505424 1
 
1.6%
1.746425942 1
 
1.6%
1.898248878 1
 
1.6%
0.5750239752 1
 
1.6%
1.057140273 1
 
1.6%
0.3841823485 1
 
1.6%
0.6528530051 1
 
1.6%
0.4962313161 1
 
1.6%
1.402855475 1
 
1.6%
Other values (52) 52
82.5%
ValueCountFrequency (%)
-0.3417621028 1
1.6%
0.11947325 1
1.6%
0.1272332308 1
1.6%
0.1670065525 1
1.6%
0.2069927937 1
1.6%
0.2174829001 1
1.6%
0.2407325332 1
1.6%
0.3027177057 1
1.6%
0.3628571149 1
1.6%
0.3841823485 1
1.6%
ValueCountFrequency (%)
2.117728792 1
1.6%
2.044287273 1
1.6%
1.965646101 1
1.6%
1.94113872 1
1.6%
1.898248878 1
1.6%
1.870308028 1
1.6%
1.746425942 1
1.6%
1.744470272 1
1.6%
1.730813749 1
1.6%
1.722965823 1
1.6%

METABOLITE 2
Real number (ℝ)

HIGH CORRELATION 

Distinct62
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.95542231
Minimum-0.27933275
Maximum2.7634306
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)1.6%
Memory size1008.0 B
2023-10-16T20:00:33.608566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.27933275
5-th percentile0.1116213
Q10.47875855
median0.91043834
Q31.4211876
95-th percentile1.9586711
Maximum2.7634306
Range3.0427634
Interquartile range (IQR)0.94242903

Descriptive statistics

Standard deviation0.64289802
Coefficient of variation (CV)0.67289408
Kurtosis0.26218664
Mean0.95542231
Median Absolute Deviation (MAD)0.48960354
Skewness0.68131509
Sum60.191606
Variance0.41331786
MonotonicityNot monotonic
2023-10-16T20:00:34.172252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6477345452 2
 
3.2%
1.168009781 1
 
1.6%
0.9882383506 1
 
1.6%
1.759292002 1
 
1.6%
0.7426445034 1
 
1.6%
1.470603106 1
 
1.6%
0.3605928493 1
 
1.6%
0.5440529801 1
 
1.6%
0.420834801 1
 
1.6%
1.269216839 1
 
1.6%
Other values (52) 52
82.5%
ValueCountFrequency (%)
-0.2793327468 1
1.6%
0.04451037679 1
1.6%
0.08172396222 1
1.6%
0.1035231807 1
1.6%
0.1845043475 1
1.6%
0.1939768149 1
1.6%
0.2102723486 1
1.6%
0.2115213948 1
1.6%
0.2397648811 1
1.6%
0.3513020315 1
1.6%
ValueCountFrequency (%)
2.763430641 1
1.6%
2.661106142 1
1.6%
2.184147999 1
1.6%
1.96297205 1
1.6%
1.919962761 1
1.6%
1.837392665 1
1.6%
1.77919319 1
1.6%
1.772577869 1
1.6%
1.759292002 1
1.6%
1.709901387 1
1.6%

METABOLITE 3
Real number (ℝ)

HIGH CORRELATION 

Distinct62
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.90098967
Minimum-0.27237786
Maximum2.5082544
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)3.2%
Memory size1008.0 B
2023-10-16T20:00:34.424889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.27237786
5-th percentile0.078484297
Q10.42240223
median0.80593674
Q31.2526599
95-th percentile1.8691919
Maximum2.5082544
Range2.7806322
Interquartile range (IQR)0.83025767

Descriptive statistics

Standard deviation0.6181465
Coefficient of variation (CV)0.68607501
Kurtosis-0.1258708
Mean0.90098967
Median Absolute Deviation (MAD)0.40610236
Skewness0.56730144
Sum56.762349
Variance0.38210509
MonotonicityNot monotonic
2023-10-16T20:00:34.682953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6224941696 2
 
3.2%
1.148858578 1
 
1.6%
1.043107675 1
 
1.6%
1.742149065 1
 
1.6%
0.6359577627 1
 
1.6%
1.342551087 1
 
1.6%
0.3330920794 1
 
1.6%
0.4623669162 1
 
1.6%
0.3577351327 1
 
1.6%
1.116631269 1
 
1.6%
Other values (52) 52
82.5%
ValueCountFrequency (%)
-0.2723778626 1
1.6%
-0.01526739441 1
1.6%
0.03469926614 1
1.6%
0.07731338913 1
1.6%
0.0890224653 1
1.6%
0.1649084444 1
1.6%
0.2053485438 1
1.6%
0.2255840386 1
1.6%
0.2581489476 1
1.6%
0.2633128159 1
1.6%
ValueCountFrequency (%)
2.508254359 1
1.6%
2.474771602 1
1.6%
2.016850715 1
1.6%
1.877674337 1
1.6%
1.79285046 1
1.6%
1.751904014 1
1.6%
1.742149065 1
1.6%
1.715012693 1
1.6%
1.678872138 1
1.6%
1.663690683 1
1.6%

METABOLITE 4
Real number (ℝ)

HIGH CORRELATION 

Distinct62
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.65158539
Minimum-0.19071126
Maximum1.7224313
Zeros0
Zeros (%)0.0%
Negative5
Negative (%)7.9%
Memory size1008.0 B
2023-10-16T20:00:34.925375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.19071126
5-th percentile-0.028085595
Q10.26909912
median0.64801957
Q31.0330405
95-th percentile1.3700101
Maximum1.7224313
Range1.9131425
Interquartile range (IQR)0.76394141

Descriptive statistics

Standard deviation0.46625865
Coefficient of variation (CV)0.71557566
Kurtosis-0.69030928
Mean0.65158539
Median Absolute Deviation (MAD)0.38670628
Skewness0.18837446
Sum41.04988
Variance0.21739713
MonotonicityNot monotonic
2023-10-16T20:00:35.160897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6480195654 2
 
3.2%
0.2768849486 1
 
1.6%
1.38070569 1
 
1.6%
1.265164501 1
 
1.6%
0.5448036739 1
 
1.6%
1.08256524 1
 
1.6%
0.3045296088 1
 
1.6%
0.4446979341 1
 
1.6%
-0.1551917685 1
 
1.6%
0.6118588566 1
 
1.6%
Other values (52) 52
82.5%
ValueCountFrequency (%)
-0.1907112596 1
1.6%
-0.1551917685 1
1.6%
-0.100157222 1
1.6%
-0.02968606408 1
1.6%
-0.01368137579 1
1.6%
0.01443570706 1
1.6%
0.04222562737 1
1.6%
0.05211005847 1
1.6%
0.1034948752 1
1.6%
0.1641874507 1
1.6%
ValueCountFrequency (%)
1.722431251 1
1.6%
1.645549807 1
1.6%
1.526578485 1
1.6%
1.38070569 1
1.6%
1.273750065 1
1.6%
1.265164501 1
1.6%
1.263096719 1
1.6%
1.18631994 1
1.6%
1.160675913 1
1.6%
1.118656997 1
1.6%

METABOLITE 5
Real number (ℝ)

HIGH CORRELATION 

Distinct62
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.099531
Minimum-0.17163036
Maximum2.2056169
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)1.6%
Memory size1008.0 B
2023-10-16T20:00:35.464269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.17163036
5-th percentile0.26982812
Q10.66999759
median1.1124538
Q31.5385034
95-th percentile2.0269436
Maximum2.2056169
Range2.3772473
Interquartile range (IQR)0.86850577

Descriptive statistics

Standard deviation0.57461052
Coefficient of variation (CV)0.52259601
Kurtosis-0.7223843
Mean1.099531
Median Absolute Deviation (MAD)0.45171347
Skewness-0.009051649
Sum69.270454
Variance0.33017725
MonotonicityNot monotonic
2023-10-16T20:00:35.727248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8267870995 2
 
3.2%
1.624077346 1
 
1.6%
2.027793246 1
 
1.6%
1.201443598 1
 
1.6%
0.7853359478 1
 
1.6%
1.629655941 1
 
1.6%
0.3358239 1
 
1.6%
0.6863028377 1
 
1.6%
0.3371107751 1
 
1.6%
1.095998173 1
 
1.6%
Other values (52) 52
82.5%
ValueCountFrequency (%)
-0.1716303562 1
1.6%
0.06946784832 1
1.6%
0.1190003537 1
1.6%
0.2624952529 1
1.6%
0.3358239 1
1.6%
0.3371107751 1
1.6%
0.3538536581 1
1.6%
0.3645903969 1
1.6%
0.3808145356 1
1.6%
0.4375093944 1
1.6%
ValueCountFrequency (%)
2.205616919 1
1.6%
2.144022415 1
1.6%
2.105736133 1
1.6%
2.027793246 1
1.6%
2.019297203 1
1.6%
1.943677038 1
1.6%
1.915992594 1
1.6%
1.886707151 1
1.6%
1.701832695 1
1.6%
1.681243048 1
1.6%

METABOLITE 6
Real number (ℝ)

HIGH CORRELATION 

Distinct62
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1954257
Minimum-0.22123189
Maximum2.4235563
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)1.6%
Memory size1008.0 B
2023-10-16T20:00:35.957839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.22123189
5-th percentile0.32966833
Q10.6727538
median1.2289467
Q31.6732326
95-th percentile2.0310971
Maximum2.4235563
Range2.6447882
Interquartile range (IQR)1.0004788

Descriptive statistics

Standard deviation0.60109761
Coefficient of variation (CV)0.50283144
Kurtosis-0.6813521
Mean1.1954257
Median Absolute Deviation (MAD)0.47345797
Skewness-0.10017744
Sum75.311817
Variance0.36131833
MonotonicityNot monotonic
2023-10-16T20:00:36.201793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.797439393 2
 
3.2%
1.702404709 1
 
1.6%
1.800547932 1
 
1.6%
1.451379158 1
 
1.6%
0.8857560083 1
 
1.6%
1.85677867 1
 
1.6%
0.3022043795 1
 
1.6%
0.7029587156 1
 
1.6%
0.581846049 1
 
1.6%
1.197824192 1
 
1.6%
Other values (52) 52
82.5%
ValueCountFrequency (%)
-0.2212318942 1
1.6%
0.09428807318 1
1.6%
0.3022043795 1
1.6%
0.3284212517 1
1.6%
0.340892059 1
1.6%
0.3544905527 1
1.6%
0.3730370397 1
1.6%
0.4390056618 1
1.6%
0.5180213768 1
1.6%
0.5329331549 1
1.6%
ValueCountFrequency (%)
2.423556319 1
1.6%
2.352050548 1
1.6%
2.226297803 1
1.6%
2.032128423 1
1.6%
2.021815077 1
1.6%
1.974420011 1
1.6%
1.963852307 1
1.6%
1.920123414 1
1.6%
1.85677867 1
1.6%
1.818054131 1
1.6%

METABOLITE 7
Real number (ℝ)

HIGH CORRELATION 

Distinct62
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.77710819
Minimum-0.08133642
Maximum1.7802098
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)1.6%
Memory size1008.0 B
2023-10-16T20:00:36.436275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.08133642
5-th percentile0.10423934
Q10.38190453
median0.69659551
Q31.1970762
95-th percentile1.4788114
Maximum1.7802098
Range1.8615462
Interquartile range (IQR)0.81517166

Descriptive statistics

Standard deviation0.45938409
Coefficient of variation (CV)0.5911456
Kurtosis-0.93374441
Mean0.77710819
Median Absolute Deviation (MAD)0.35983428
Skewness0.14528647
Sum48.957816
Variance0.21103374
MonotonicityNot monotonic
2023-10-16T20:00:36.662032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6952694334 2
 
3.2%
1.344521711 1
 
1.6%
1.187991715 1
 
1.6%
1.288260532 1
 
1.6%
0.5779375849 1
 
1.6%
1.780209809 1
 
1.6%
0.3367612265 1
 
1.6%
0.4992779121 1
 
1.6%
0.2876704767 1
 
1.6%
1.401200877 1
 
1.6%
Other values (52) 52
82.5%
ValueCountFrequency (%)
-0.08133642022 1
1.6%
0.003278036331 1
1.6%
0.06623800745 1
1.6%
0.1035673955 1
1.6%
0.110286803 1
1.6%
0.1384182605 1
1.6%
0.1728822138 1
1.6%
0.2498801238 1
1.6%
0.2661718799 1
1.6%
0.2876704767 1
1.6%
ValueCountFrequency (%)
1.780209809 1
1.6%
1.648490792 1
1.6%
1.497260586 1
1.6%
1.486059917 1
1.6%
1.41357495 1
1.6%
1.401200877 1
1.6%
1.366384578 1
1.6%
1.361281265 1
1.6%
1.344521711 1
1.6%
1.314890293 1
1.6%

METABOLITE 8
Real number (ℝ)

HIGH CORRELATION 

Distinct62
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.62541012
Minimum-0.22356176
Maximum1.2603071
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)1.6%
Memory size1008.0 B
2023-10-16T20:00:36.906519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.22356176
5-th percentile0.1301589
Q10.41250287
median0.66793719
Q30.85513177
95-th percentile1.1027304
Maximum1.2603071
Range1.4838689
Interquartile range (IQR)0.4426289

Descriptive statistics

Standard deviation0.31908778
Coefficient of variation (CV)0.51020566
Kurtosis-0.36007162
Mean0.62541012
Median Absolute Deviation (MAD)0.22747896
Skewness-0.1282078
Sum39.400837
Variance0.10181701
MonotonicityNot monotonic
2023-10-16T20:00:37.133094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8503277649 2
 
3.2%
0.4404582373 1
 
1.6%
1.219563792 1
 
1.6%
0.7570396338 1
 
1.6%
0.123016882 1
 
1.6%
0.9094578234 1
 
1.6%
0.3719182158 1
 
1.6%
0.5319592418 1
 
1.6%
-0.223561757 1
 
1.6%
1.102859764 1
 
1.6%
Other values (52) 52
82.5%
ValueCountFrequency (%)
-0.223561757 1
1.6%
0.07931222689 1
1.6%
0.123016882 1
1.6%
0.1274176614 1
1.6%
0.1548300331 1
1.6%
0.1665241093 1
1.6%
0.2386631908 1
1.6%
0.2602056146 1
1.6%
0.263541066 1
1.6%
0.2827378625 1
1.6%
ValueCountFrequency (%)
1.260307123 1
1.6%
1.221233127 1
1.6%
1.219563792 1
1.6%
1.102859764 1
1.6%
1.101566016 1
1.6%
1.100666264 1
1.6%
1.01239482 1
1.6%
0.9747052835 1
1.6%
0.9527431064 1
1.6%
0.9382930567 1
1.6%

METABOLITE 9
Real number (ℝ)

HIGH CORRELATION 

Distinct62
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2423541
Minimum0.32741782
Maximum2.4246493
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1008.0 B
2023-10-16T20:00:37.388216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.32741782
5-th percentile0.59476902
Q10.99395959
median1.190196
Q31.5252404
95-th percentile1.8927713
Maximum2.4246493
Range2.0972315
Interquartile range (IQR)0.53128079

Descriptive statistics

Standard deviation0.4187418
Coefficient of variation (CV)0.3370551
Kurtosis0.01458626
Mean1.2423541
Median Absolute Deviation (MAD)0.27970793
Skewness0.2197144
Sum78.268311
Variance0.17534469
MonotonicityNot monotonic
2023-10-16T20:00:37.620353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9104880547 2
 
3.2%
1.705456312 1
 
1.6%
1.148468385 1
 
1.6%
1.833443774 1
 
1.6%
0.7016894266 1
 
1.6%
1.736581489 1
 
1.6%
0.7743998124 1
 
1.6%
0.9205773494 1
 
1.6%
1.03528029 1
 
1.6%
2.424649291 1
 
1.6%
Other values (52) 52
82.5%
ValueCountFrequency (%)
0.3274178215 1
1.6%
0.5021733563 1
1.6%
0.5318009802 1
1.6%
0.5930475822 1
1.6%
0.6102619113 1
1.6%
0.6245954498 1
1.6%
0.6926332642 1
1.6%
0.7016894266 1
1.6%
0.7665902962 1
1.6%
0.7743998124 1
1.6%
ValueCountFrequency (%)
2.424649291 1
1.6%
2.006175958 1
1.6%
1.956374199 1
1.6%
1.894714541 1
1.6%
1.875282028 1
1.6%
1.833443774 1
1.6%
1.826756332 1
1.6%
1.736581489 1
1.6%
1.705456312 1
1.6%
1.699792783 1
1.6%

METABOLITE 10
Real number (ℝ)

HIGH CORRELATION 

Distinct62
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1932259
Minimum0.2670208
Maximum2.3749549
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1008.0 B
2023-10-16T20:00:37.853864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.2670208
5-th percentile0.60852653
Q10.91571584
median1.1074726
Q31.4435588
95-th percentile1.8588647
Maximum2.3749549
Range2.1079341
Interquartile range (IQR)0.52784296

Descriptive statistics

Standard deviation0.41590822
Coefficient of variation (CV)0.34855781
Kurtosis0.087314065
Mean1.1932259
Median Absolute Deviation (MAD)0.25880267
Skewness0.40190622
Sum75.173235
Variance0.17297965
MonotonicityNot monotonic
2023-10-16T20:00:38.103378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9148409446 2
 
3.2%
1.615769905 1
 
1.6%
1.023534561 1
 
1.6%
1.796889974 1
 
1.6%
0.7312540017 1
 
1.6%
1.768543444 1
 
1.6%
0.715195754 1
 
1.6%
0.8486698946 1
 
1.6%
0.8952580961 1
 
1.6%
2.374954875 1
 
1.6%
Other values (52) 52
82.5%
ValueCountFrequency (%)
0.2670207981 1
1.6%
0.4960677236 1
1.6%
0.6080727942 1
1.6%
0.6081129174 1
1.6%
0.6122490334 1
1.6%
0.6482799838 1
1.6%
0.6899459117 1
1.6%
0.692438447 1
1.6%
0.715195754 1
1.6%
0.7312540017 1
1.6%
ValueCountFrequency (%)
2.374954875 1
1.6%
2.068447447 1
1.6%
1.888541311 1
1.6%
1.859882498 1
1.6%
1.849704851 1
1.6%
1.821805169 1
1.6%
1.796889974 1
1.6%
1.768543444 1
1.6%
1.702748313 1
1.6%
1.615769905 1
1.6%

METABOLITE 11
Real number (ℝ)

HIGH CORRELATION 

Distinct62
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8887854
Minimum-0.088224226
Maximum1.8531871
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)3.2%
Memory size1008.0 B
2023-10-16T20:00:38.393965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.088224226
5-th percentile0.15369534
Q10.58773061
median0.8524835
Q31.2158811
95-th percentile1.6399599
Maximum1.8531871
Range1.9414113
Interquartile range (IQR)0.6281505

Descriptive statistics

Standard deviation0.44524315
Coefficient of variation (CV)0.50095686
Kurtosis-0.1858926
Mean0.8887854
Median Absolute Deviation (MAD)0.30250764
Skewness0.12432632
Sum55.99348
Variance0.19824146
MonotonicityNot monotonic
2023-10-16T20:00:38.669809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.8050952 2
 
3.2%
0.08689345527 1
 
1.6%
0.8106419898 1
 
1.6%
0.4234352486 1
 
1.6%
0.8469795714 1
 
1.6%
0.3763952801 1
 
1.6%
1.614496219 1
 
1.6%
1.239796855 1
 
1.6%
0.5810567919 1
 
1.6%
0.5931478168 1
 
1.6%
Other values (52) 52
82.5%
ValueCountFrequency (%)
-0.08822422564 1
1.6%
-0.07297774744 1
1.6%
0.08689345527 1
1.6%
0.1447927449 1
1.6%
0.2338186857 1
1.6%
0.3763952801 1
1.6%
0.3879272998 1
1.6%
0.4234352486 1
1.6%
0.4459865642 1
1.6%
0.5078308072 1
1.6%
ValueCountFrequency (%)
1.853187114 1
1.6%
1.8050952 2
3.2%
1.642789191 1
1.6%
1.614496219 1
1.6%
1.498424904 1
1.6%
1.468575874 1
1.6%
1.452456944 1
1.6%
1.452139187 1
1.6%
1.376807081 1
1.6%
1.324225383 1
1.6%

METABOLITE 12
Real number (ℝ)

HIGH CORRELATION 

Distinct62
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0446786
Minimum0.12922127
Maximum4.4270479
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1008.0 B
2023-10-16T20:00:38.954870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.12922127
5-th percentile0.40495297
Q10.87132542
median1.9571121
Q33.2611742
95-th percentile3.9684518
Maximum4.4270479
Range4.2978266
Interquartile range (IQR)2.3898488

Descriptive statistics

Standard deviation1.2656039
Coefficient of variation (CV)0.61897449
Kurtosis-1.3497562
Mean2.0446786
Median Absolute Deviation (MAD)1.1628618
Skewness0.2530058
Sum128.81475
Variance1.6017532
MonotonicityNot monotonic
2023-10-16T20:00:39.441141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.156737451 2
 
3.2%
0.1292212727 1
 
1.6%
0.4652429119 1
 
1.6%
0.6682078702 1
 
1.6%
3.551957424 1
 
1.6%
0.7699194017 1
 
1.6%
3.971197161 1
 
1.6%
3.855269745 1
 
1.6%
1.59032944 1
 
1.6%
0.9074228015 1
 
1.6%
Other values (52) 52
82.5%
ValueCountFrequency (%)
0.1292212727 1
1.6%
0.219430742 1
1.6%
0.3818458779 1
1.6%
0.4038764435 1
1.6%
0.4146417386 1
1.6%
0.4652429119 1
1.6%
0.5911517922 1
1.6%
0.6682078702 1
1.6%
0.6775884015 1
1.6%
0.6782741967 1
1.6%
ValueCountFrequency (%)
4.427047886 1
1.6%
4.080044389 1
1.6%
4.070067052 1
1.6%
3.971197161 1
1.6%
3.943743221 1
1.6%
3.898298483 1
1.6%
3.855269745 1
1.6%
3.82597061 1
1.6%
3.64229662 1
1.6%
3.617075873 1
1.6%

METABOLITE 13
Real number (ℝ)

HIGH CORRELATION 

Distinct62
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0748521
Minimum0.013354043
Maximum4.3240015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1008.0 B
2023-10-16T20:00:39.674329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.013354043
5-th percentile0.37509369
Q10.84891921
median2.1117482
Q33.3136356
95-th percentile4.0252367
Maximum4.3240015
Range4.3106475
Interquartile range (IQR)2.4647164

Descriptive statistics

Standard deviation1.3229247
Coefficient of variation (CV)0.63759954
Kurtosis-1.3901066
Mean2.0748521
Median Absolute Deviation (MAD)1.2508419
Skewness0.19236091
Sum130.71568
Variance1.7501298
MonotonicityNot monotonic
2023-10-16T20:00:39.906810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.152552766 2
 
3.2%
0.08275708359 1
 
1.6%
0.4508390168 1
 
1.6%
0.5536602937 1
 
1.6%
3.74283179 1
 
1.6%
0.745220536 1
 
1.6%
4.06781263 1
 
1.6%
3.988787131 1
 
1.6%
1.707582289 1
 
1.6%
0.9166664347 1
 
1.6%
Other values (52) 52
82.5%
ValueCountFrequency (%)
0.01335404341 1
1.6%
0.08275708359 1
1.6%
0.3128119535 1
1.6%
0.3750904271 1
1.6%
0.375123032 1
1.6%
0.4508390168 1
1.6%
0.5013518912 1
1.6%
0.5205206693 1
1.6%
0.5536602937 1
1.6%
0.5786842961 1
1.6%
ValueCountFrequency (%)
4.324001509 1
1.6%
4.273676468 1
1.6%
4.06781263 1
1.6%
4.02548252 1
1.6%
4.023024185 1
1.6%
3.991449882 1
1.6%
3.988787131 1
1.6%
3.916346157 1
1.6%
3.74283179 1
1.6%
3.721474999 1
1.6%

METABOLITE 14
Real number (ℝ)

HIGH CORRELATION 

Distinct62
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.065536
Minimum0.11781349
Maximum2.2196735
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1008.0 B
2023-10-16T20:00:40.132614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.11781349
5-th percentile0.31698816
Q10.66029079
median1.0815705
Q31.3610172
95-th percentile1.714675
Maximum2.2196735
Range2.10186
Interquartile range (IQR)0.70072642

Descriptive statistics

Standard deviation0.45644955
Coefficient of variation (CV)0.42837555
Kurtosis-0.41967244
Mean1.065536
Median Absolute Deviation (MAD)0.34470156
Skewness0.089290969
Sum67.128765
Variance0.20834619
MonotonicityNot monotonic
2023-10-16T20:00:40.368577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.081570539 2
 
3.2%
0.1178134885 1
 
1.6%
0.4081562018 1
 
1.6%
0.424662783 1
 
1.6%
1.719948651 1
 
1.6%
0.8078857162 1
 
1.6%
1.303957758 1
 
1.6%
0.9472256032 1
 
1.6%
1.931235895 1
 
1.6%
0.8562774188 1
 
1.6%
Other values (52) 52
82.5%
ValueCountFrequency (%)
0.1178134885 1
1.6%
0.2250277419 1
1.6%
0.3033446822 1
1.6%
0.3068583771 1
1.6%
0.4081562018 1
1.6%
0.424662783 1
1.6%
0.4989354312 1
1.6%
0.5446058994 1
1.6%
0.5714940241 1
1.6%
0.5940008281 1
1.6%
ValueCountFrequency (%)
2.219673494 1
1.6%
1.939931371 1
1.6%
1.931235895 1
1.6%
1.719948651 1
1.6%
1.66721243 1
1.6%
1.648141719 1
1.6%
1.609521448 1
1.6%
1.58428774 1
1.6%
1.562883935 1
1.6%
1.562509755 1
1.6%

TYPE
Categorical

Distinct3
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size1008.0 B
MENINGIOMA
36 
ASTROCYTOMA
16 
GLIOBLASTOMA
11 

Length

Max length12
Median length10
Mean length10.603175
Min length10

Characters and Unicode

Total characters668
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowASTROCYTOMA
2nd rowMENINGIOMA
3rd rowASTROCYTOMA
4th rowMENINGIOMA
5th rowMENINGIOMA

Common Values

ValueCountFrequency (%)
MENINGIOMA 36
57.1%
ASTROCYTOMA 16
25.4%
GLIOBLASTOMA 11
 
17.5%

Length

2023-10-16T20:00:40.593019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-16T20:00:40.825455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
meningioma 36
57.1%
astrocytoma 16
25.4%
glioblastoma 11
 
17.5%

Most occurring characters

ValueCountFrequency (%)
M 99
14.8%
O 90
13.5%
A 90
13.5%
I 83
12.4%
N 72
10.8%
G 47
7.0%
T 43
6.4%
E 36
 
5.4%
S 27
 
4.0%
L 22
 
3.3%
Other values (4) 59
8.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 668
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 99
14.8%
O 90
13.5%
A 90
13.5%
I 83
12.4%
N 72
10.8%
G 47
7.0%
T 43
6.4%
E 36
 
5.4%
S 27
 
4.0%
L 22
 
3.3%
Other values (4) 59
8.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 668
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 99
14.8%
O 90
13.5%
A 90
13.5%
I 83
12.4%
N 72
10.8%
G 47
7.0%
T 43
6.4%
E 36
 
5.4%
S 27
 
4.0%
L 22
 
3.3%
Other values (4) 59
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 668
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 99
14.8%
O 90
13.5%
A 90
13.5%
I 83
12.4%
N 72
10.8%
G 47
7.0%
T 43
6.4%
E 36
 
5.4%
S 27
 
4.0%
L 22
 
3.3%
Other values (4) 59
8.8%

Interactions

2023-10-16T20:00:19.815514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:25.290295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:28.544289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:31.594924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:35.118147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:38.467216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:41.795419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:44.641946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:47.277417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:54.639890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:01.914254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:04.458615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:07.016000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:14.427582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:17.129194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:20.001361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:25.483221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:28.692421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:31.777260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:35.328601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:38.675341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:41.943903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:44.802557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:47.554580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:54.816097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:02.072304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:04.637267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:07.399648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:14.573967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:17.275953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:20.182709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-10-16T20:00:20.556441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:26.034928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:29.271078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:32.322586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:36.103481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:39.427656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:42.423145image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:45.289009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:48.166331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:55.505355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:02.630494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:05.102532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:10.803740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:15.137956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:17.872846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:20.760586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:26.219896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:29.505201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:32.559805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:36.305110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:39.691910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:42.613521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:45.476617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:49.345926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:55.693430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-10-16T20:00:05.408230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:12.130688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:15.324980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:18.129024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:20.928607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:26.372077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:29.661042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:32.724669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:36.497501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:39.870405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:42.868121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:45.628613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:50.450221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:55.852287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:02.965073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:05.563846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:12.418393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:15.469696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:18.364039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:21.079495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:26.536591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:29.810035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:32.908112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:36.760899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:40.070885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:43.095678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:45.786579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:51.582873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:56.017842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:03.126751image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:05.713803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:12.586463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:15.621116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:18.530570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:21.280016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:26.803112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:29.989547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:33.242328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:36.985525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:40.339797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:43.270030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:45.965576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:52.767368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:56.200884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:03.307439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:05.883094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:12.769054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:15.783427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:18.717632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:21.464259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:27.132025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:30.175412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:33.550461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:37.164678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:40.713807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:43.458281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:46.163570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:53.442344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:56.386304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:03.483294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:06.046990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:12.989540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:15.951551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:18.890941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:21.609596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:27.385524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:30.405226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:33.867466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:37.321538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:40.881746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:43.728314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:46.383459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:53.616709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:56.638219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:03.645883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:06.201334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:13.250162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:16.097299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:19.053933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:21.749121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:27.541891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:30.596005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:34.077389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:37.467870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:41.044620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:43.984159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:46.530967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:53.774066image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:57.963053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:03.791121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:06.341710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:13.479155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:16.240876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:19.197855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:21.945619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:27.825589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:30.844203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:34.326476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:37.655372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:41.296404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:44.180259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:46.702075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:53.973245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:59.435865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:03.965326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:06.522058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:13.681757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:16.412117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:19.368425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:22.122137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:28.008773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:31.105683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:34.628874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:37.919724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:41.469668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:44.336546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:46.867757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:54.134130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:00.500266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:04.123839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:06.666383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:13.856939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:16.673682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:19.520068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:22.508184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:28.389602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:31.364514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:34.903648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:38.199182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:41.636767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:44.489641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:47.024277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T19:59:54.375887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:01.606088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:04.278197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:06.814955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:14.268629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:16.939226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-16T20:00:19.675703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-10-16T20:00:41.004747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
METABOLITE 0METABOLITE 1METABOLITE 2METABOLITE 3METABOLITE 4METABOLITE 5METABOLITE 6METABOLITE 7METABOLITE 8METABOLITE 9METABOLITE 10METABOLITE 11METABOLITE 12METABOLITE 13METABOLITE 14TYPE
METABOLITE 01.0000.8100.7150.7020.6850.6960.6680.6620.6070.4760.517-0.489-0.772-0.775-0.5810.193
METABOLITE 10.8101.0000.8030.7960.8080.7720.7310.6840.6810.5190.567-0.478-0.752-0.763-0.5850.367
METABOLITE 20.7150.8031.0000.9920.8190.8410.8690.8890.6360.6380.691-0.566-0.818-0.823-0.6250.443
METABOLITE 30.7020.7960.9921.0000.8190.8490.8750.8890.6490.6320.684-0.566-0.808-0.816-0.6360.448
METABOLITE 40.6850.8080.8190.8191.0000.7860.7100.7160.7610.4610.508-0.351-0.721-0.736-0.7160.443
METABOLITE 50.6960.7720.8410.8490.7861.0000.9670.7880.7220.6560.691-0.539-0.784-0.787-0.6020.431
METABOLITE 60.6680.7310.8690.8750.7100.9671.0000.8160.6530.6890.728-0.572-0.763-0.762-0.5200.434
METABOLITE 70.6620.6840.8890.8890.7160.7880.8161.0000.6550.6640.708-0.481-0.740-0.752-0.5780.343
METABOLITE 80.6070.6810.6360.6490.7610.7220.6530.6551.0000.5890.618-0.163-0.587-0.614-0.5740.358
METABOLITE 90.4760.5190.6380.6320.4610.6560.6890.6640.5891.0000.972-0.383-0.601-0.594-0.3440.244
METABOLITE 100.5170.5670.6910.6840.5080.6910.7280.7080.6180.9721.000-0.393-0.636-0.631-0.3960.235
METABOLITE 11-0.489-0.478-0.566-0.566-0.351-0.539-0.572-0.481-0.163-0.383-0.3931.0000.6770.6440.3430.363
METABOLITE 12-0.772-0.752-0.818-0.808-0.721-0.784-0.763-0.740-0.587-0.601-0.6360.6771.0000.9950.6750.408
METABOLITE 13-0.775-0.763-0.823-0.816-0.736-0.787-0.762-0.752-0.614-0.594-0.6310.6440.9951.0000.7040.468
METABOLITE 14-0.581-0.585-0.625-0.636-0.716-0.602-0.520-0.578-0.574-0.344-0.3960.3430.6750.7041.0000.459
TYPE0.1930.3670.4430.4480.4430.4310.4340.3430.3580.2440.2350.3630.4080.4680.4591.000

Missing values

2023-10-16T20:00:24.312427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-16T20:00:27.440608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

METABOLITE 0METABOLITE 1METABOLITE 2METABOLITE 3METABOLITE 4METABOLITE 5METABOLITE 6METABOLITE 7METABOLITE 8METABOLITE 9METABOLITE 10METABOLITE 11METABOLITE 12METABOLITE 13METABOLITE 14TYPE
611.1501051.4724851.5781411.6636911.2630971.4297571.5089801.3612810.9527431.2636551.097508-0.0729780.2194310.0133540.306858ASTROCYTOMA
281.5567921.2618851.7791931.6566930.8943531.8867072.0321281.2618990.7525341.8947151.8598820.5274130.7942500.8609060.943798MENINGIOMA
641.2727641.4551111.5942301.6192191.0260452.1057362.2262981.2090571.0123951.5465671.5271330.8409550.6782740.6042550.750615ASTROCYTOMA
310.5824530.7270790.4982230.4100440.2180670.7770480.7641480.3771800.4410641.0038310.8544811.2167163.5391103.6861281.462321MENINGIOMA
401.7562002.0442872.6611062.4747721.6455501.3412461.5052801.1944080.8288411.3808301.4319950.2338190.4038760.3128120.225028MENINGIOMA
440.1579400.2407330.1035230.0890220.0521100.3808150.3544910.2947960.2960300.7665900.6899461.3242253.8259713.9163461.477651MENINGIOMA
881.6481981.9656460.9136880.9253841.0400361.3449631.3131330.6355870.9747051.4779501.4219180.6430541.2331301.2540601.052289GLIOBLASTOMA
601.2770401.4727881.4316381.2932810.7530371.5641671.9638521.6484910.7031611.4817371.3493040.1447931.3894741.4896180.979590ASTROCYTOMA
79-0.2557051.2968870.8289820.8059370.8556801.4403731.3422720.3734150.8599361.5575351.5368730.8044931.4190391.4607860.943078GLIOBLASTOMA
680.7505800.9161410.9783020.9521830.7005521.1628821.4750210.7649550.7105551.3570271.3231901.2150462.5906502.5260951.264971ASTROCYTOMA
METABOLITE 0METABOLITE 1METABOLITE 2METABOLITE 3METABOLITE 4METABOLITE 5METABOLITE 6METABOLITE 7METABOLITE 8METABOLITE 9METABOLITE 10METABOLITE 11METABOLITE 12METABOLITE 13METABOLITE 14TYPE
500.4318740.8453600.5631950.3969920.6052031.0312231.0340790.9947470.6728381.2697481.2033621.8531872.3816242.2463181.255168MENINGIOMA
410.5467760.4246690.6004140.5562370.3488840.6046970.5778890.5062820.5641800.9956521.0452611.1303812.8767863.1478571.168127MENINGIOMA
781.3722172.1177291.7725781.7519041.7224311.9436771.7916920.9504730.7882741.0805651.0247860.3879270.8050250.8327440.657418GLIOBLASTOMA
571.0365561.4216542.1841482.0168511.1863202.1440222.3520510.9836731.1015661.5449751.4645800.6574840.5911520.5205210.544606ASTROCYTOMA
531.2622961.0815101.4462451.4013870.9373401.5119681.6403861.0949290.6679371.0575151.0799770.5078310.4146420.3750900.303345ASTROCYTOMA
771.3391471.6024321.0782561.0936091.1606762.0192971.9744201.2139091.2212331.1462031.1498940.9529221.1242931.0742250.822696GLIOBLASTOMA
670.8951520.6663950.6347200.4729150.2328070.4581580.5495490.1728820.0793121.1862820.9351680.6292201.3141871.4404041.250559ASTROCYTOMA
37-0.0243160.1194730.044510-0.0152670.0422260.0694680.0942880.1384180.2386630.5021730.6482801.4984253.2748773.3096501.186551MENINGIOMA
701.2736951.7229661.8373931.7928501.0440822.2056172.4235561.2648380.7269351.5514821.4611060.5466570.6954010.5013520.629976ASTROCYTOMA
21.4528701.1529100.6477350.6224940.6480200.8267870.7974390.6952690.8503280.9104880.9148411.8050952.1567372.1525531.081571MENINGIOMA

Duplicate rows

Most frequently occurring

METABOLITE 0METABOLITE 1METABOLITE 2METABOLITE 3METABOLITE 4METABOLITE 5METABOLITE 6METABOLITE 7METABOLITE 8METABOLITE 9METABOLITE 10METABOLITE 11METABOLITE 12METABOLITE 13METABOLITE 14TYPE# duplicates
01.452871.152910.6477350.6224940.648020.8267870.7974390.6952690.8503280.9104880.9148411.8050952.1567372.1525531.081571MENINGIOMA2